15 papers · ranked by Valyu relevance
Rong Huang, Su Tao
Automated Machine Learning (AutoML) aims to streamline the end-to-end process of ML models, yet current approaches remain constrained by rigid rule-based frameworks and structured input requirements that create barriers for non-expert users. Despite advances in Large Language Models (LLMs) demonstrating capabilities in…
Dan Tulpan, Luis O Tedeschi, Hector Menendez, Ricardo Augusto M Vieira
Integrating open-source tools and machine learning (ML) pipelines into livestock data analysis transforms research, education, and decision-making in animal science. This study presents a comprehensive, end-to-end regression pipeline implemented in Python, designed to predict outcome variables from structured input…
Stephen Coshatt, He Yang, Shushan Wu, Jin Ye + 5 more
As machine learning and artificial intelligence are being integrated into cyber-physical systems, it is becoming important for engineers to know and understand these topics. In particular, sensor data is on the rise in these systems and therefore engineers need to understand which models are appropriate to time-series…
Melih Peker, Ozcan Ozturk
Selecting a good set of optimization flags requires extensive effort and expert input. While most of the prior research considers using static, spatial, or dynamic features, some of the latest research directly applied deep neural networks to source code. We combined the static features, spatial features, and deep…
M. Shahbaz Ismail, Sara Shahzad, Fahmi H. Quradaa, Sajid Anwar
Semantic code clone detection plays an essential role in software maintenance and quality assurance, as it helps uncover fragments of code that express the same logic even when their syntax has been altered or deliberately obfuscated. In this study, we propose a framework that combines hybrid representation learning…
Huifang Ma, Zhicheng Ji, Tara Al-Hashimy, Austin Allen + 31 more
Title: Significance Large language models (LLMs) are increasingly used in science and engineering, yet their real-world effectiveness in data analysis remains unclear. In this study, graduate students used LLMs to tackle biomedical data challenges on Kaggle, a popular data science platform. Despite limited programming…
Meryem Gharmili, Youssef Aatif, Alj Abdelkamel
Generative AI coding assistants are increasingly used to write machine-learning code, yet their ability to produce reliable LSTM implementations for financial prediction remains underexplored. This study evaluates the LSTM code generated by seven assistants ChatGPT 4.5, GitHub Copilot, Deepseek 3, Perplexity, Gemini…
Vlastimil Martinek, Andrea Gariboldi, Dimosthenis Tzimotoudis, Mark Galea + 7 more
The past decades have witnessed the transformation of molecular biology into a truly data-driven science, in large part due to the growth in the quantity and variety of molecular biology data generated by technologies such as mass spectrometry and high-throughput sequencing (, ). An important step in the analysis…
Eric W. Bridgeford, Iain Declan Campbell, Zijiao Chen, Zhicheng Lin + 4 more
While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific validity. In this paper, we provide twelve practical tips for AI-assisted coding that balance the capabilities of AI with the demands of…
Marvin van Aalst, Tim Nies, Tobias Pfennig, Anna Matuszyńska + 1 more
Mathematical modelling has evolved into a cornerstone of modern biological research, offering a rigorous, quantitative framework to explore the complexity of living systems. However, the ongoing artificial intelligence revolution is reshaping the computational biology landscape, where small-scale mechanistic models are…
Osvaldo Velazquez-Gonzalez, Antonio Alarcón-Paredes, Cornelio Yañez-Marquez
Classification is a central task in machine learning, underpinning applications in domains such as finance, medicine, engineering, information technology, and biology. However, machine learning pattern classification can become a complex or even inexplicable task for current robust models due to the complexity of…
Latika, Renuka Arora
Assisted Reproductive Techniques (ART) refers to the reproductive measures that address the issues like infertility, low sperm count, unable to conceive and help the couples to achieve pregnancy. Various Assisted Reproductive techniques like in-vitro Fertilization, Intrauterine Insemination are used to treat patients…
Arafat Rohan, Md. Deluar Hossen, Md. Nuruzzaman Pranto, Balayet Hossain + 2 more
This study reviews the advancements in AI-driven methods for predicting stock prices, tracing their evolution from traditional approaches to modern finance. The role of AI in the market extends beyond predictive systems to encompass the intersection of financial markets with emerging technologies, such as blockchain…
Firi Ziyad, Habtamu Alemayehu, Desalegn Wogaso, Getachew Semegn + 4 more
This research examined the performance of a machine learning algorithm when predicting the surface roughness of tempered steel AISI 1060. Different machine learning algorithms, such as decision tree (DT), random forest (RF), adaptive boosting (ADB), gradient boosting (GB), and extreme gradient boosting (XGB), were…
Qingyu Zhao, Kilian M. Pohl
PURPOSE The review surveys the type of machine learning approaches currently used in the alcohol literature, reviews challenges in applying machine learning tools to alcohol data, and explores how overcoming these challenges could advance personalized medicine for alcohol use disorder (AUD). SEARCH METHODS The authors…